DETAILED ACTION
The Applicant’s responses, received 09 April 2026 and 10 April 2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant's election with traverse of Group I (claims 1-58) in the reply filed on 09 April 2026 is acknowledged. The traversal is on the ground(s) that Group I and Group II are not patentably distinct because the computer-readable medium of Group II simply provides the instructions for performing the method of Group I, and that the claims differ only in statutory class, but otherwise share the same functions, and as such, searching both would not pose an undue burden to the Office.
This is not found persuasive because the method of predictive modeling (i.e., the process) in Group I does not require the computer-readable medium (i.e., machine or manufacture) in Group II, and therefore the classification of each group is different, and the search and consideration of each group is different and unique. If Group I is found to be allowable subject matter, then Groups I and II will be rejoined.
Applicant's election with traverse of Species I (outputting a model that is derived from the analysis of claim 1, as recited in claims 2-14) in the reply filed on 09 April 2026 is acknowledged. The traversal is on the ground(s) that the identified species 1-5 are not patentably distinct because they all share a common technical feature: a method for outputting predictive models for cell or gene therapy (CGT) by integrating machine learning and mechanistic models, and while the application of the model varies by therapy type, the underlying data-driven process development remains the same across all species, and therefore, the species represent a single inventive concept, and a search of the generic claims would encompass the subject matter of all identified species without creating an undue search burden.
This is not found persuasive because each would require a separate search and consideration, and each therapy would require unique modeling. Additionally, these species are not obvious variants of each other based on the current record.
Applicant's election without traverse of a specific output model (i.e., algebraic learning via elastic net (ALVEN) in claim 9) and a specific stable cell line (HEK296 cells in claim 10) is acknowledged.
The requirement is still deemed proper and is therefore made FINAL.
Status of the Claims
Claims 1-59 are pending.
Claims 15-59 are withdrawn.
Claims 1-14 are rejected.
Priority
There are no domestic or foreign applications for which benefit is claimed.
Therefore, the effective filing date of the claimed invention is 04 October 2022.
Information Disclosure Statement
The information disclosure statements (IDS) received 10 January 2023, 21 December 2023, 08 May 2025, 08 August 2025, and 24 December 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 04 October 2022 are objected to for the reasons noted below.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
reference 209 in Fig. 2.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is:
data processing system in claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The written description discloses a corresponding structure for the generic placeholder:
data processing system in claim 1, at paragraph [0091] in the Specification and Fig. 8 in the drawings (i.e., a computer system).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-14 recite a method implemented by a data processing system for outputting one or more models (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining one or more attributes of the plurality of data items (i.e., mental processes);
selecting one or more machine learning models based on the one or more attributes (i.e., mental processes);
accessing one or more mechanistic models (i.e., mental processes);
integrating the one or more machine learning models with the one or more mechanistic models to obtain one or more integrated models (i.e., mental processes);
selecting one or more predictive models from the one or more machine learning models, the one or more mechanistic models, and the one or more integrated models (i.e., mental processes);
applying the one or more predictive models to the plurality of data items (i.e., mental processes and mathematical concepts); and
adjusting one or more values of one or more parameters of the one or more predictive models to reduce uncertainty in model prediction (i.e., mental processes and mathematical concepts).
Dependent claims 2-6, 9, 12, and 13 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
the one or more attributes comprise at least one of: nonlinearity; collinearity; nonnormality; or dynamics (i.e., mental processes).
Dependent claim 3 further recites:
the one or more mechanistic models are accessed based on at least one of a physical property, a chemical property, or a biological property of the process (i.e., mental processes).
Dependent claim 4 further recites:
arranging the one or more machine learning models and the one or more mechanistic models in a sequence comprising a first one or more models and a second one or more models (i.e., mental processes); and
obtaining an output of the second one or more models (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
determining a first one or more models and a second one or more models from the one or more machine learning models and the one or more mechanistic models (i.e., mental processes);
constraining a prediction of the first one or more models using the second one or more models (i.e., mental processes and mathematical concepts); and
obtaining an output of the first one or more models (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
using one or more output models (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
using one or more algebraic learning via elastic net (ALVEN) output models (i.e., mental processes and mathematical concepts).
Dependent claim 12 further recites:
uses one or more output models (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
uses one or more output models (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., determining one or more attributes of the plurality of data items (i.e., mental processes), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying the one or more predictive models to the plurality of data items) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-14 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2 and 3 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
receiving a plurality of data items (i.e., receiving data);
storing the plurality of data items (i.e., storing data);
a hardware storage device;
accessing the plurality of data items (i.e., accessing data);
a data processing system (i.e., a computer system); and
outputting the one or more predictive models with the one or more adjusted values of the one or more parameters (i.e., outputting data).
The additional elements in dependent claim 4-14 include:
transmitting an output of the first one or more models to the second one or more models (i.e., transmitting data) (claim 4);
transmitting data to the second one or more models (i.e., transmitting data) (claim 4);
transmitting input data to the first one or more models (i.e., transmitting data) (claim 5);
the plurality of data items is obtained from a cell population of a first type (i.e., obtaining data (claim 6);
causing production of a cell population of a second type wherein the second type is different from the first type (claim 6);
each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7);
the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8);
causing production of a stable cell line (claim 9);
the stable cell line comprises HEK293 cells (claim 10);
a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11);
cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12);
an automated or semi-automated production (claim 13); and
the production is in a closed or semi-closed system (claim 14).
The additional elements of a data processing system (i.e., a computer system) (claim 1); and a hardware storage device (claim 1); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of receiving a plurality of data items (i.e., receiving data) (claim 1); storing the plurality of data items (i.e., storing data) (claim 1); accessing the plurality of data items (i.e., accessing data) (claim 1); outputting the one or more predictive models with the one or more adjusted values of the one or more parameters (i.e., outputting data) (claim 1); transmitting an output of the first one or more models to the second one or more models (i.e., transmitting data) (claim 4); transmitting data to the second one or more models (i.e., transmitting data) (claim 4); transmitting input data to the first one or more models (i.e., transmitting data) (claim 5); and the plurality of data items is obtained from a cell population of a first type (i.e., obtaining data (claim 6); are merely pre-solution and/or post-solution activities used in the claimed process – nominal or tangential additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of causing production of a cell population of a second type wherein the second type is different from the first type (claim 6); each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7); the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8); causing production of a stable cell line (claim 9); the stable cell line comprises HEK293 cells (claim 10); a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11); cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12); an automated or semi-automated production (claim 13); and the production is in a closed or semi-closed system (claim 14); amount to mere instructions to apply an exception, because these types of limitations are equivalent to the words “apply it.” These claim limitations attempt to cover any solution to the one or more predictive models with the one or more adjusted values of the one or more parameters, with no restriction on the output of the one or more predictive models to cause production of a cell line, because the limitation of using one or more predictive models could mean any using any model output, since there is not a restriction, limit, or indication as to what the output model actually comprises and how it relates to the steps of the production of a cell population. Therefore, these additional elements do not integrate the judicial exceptions into a practical application (MPEP 2106.05(f)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or amount to mere instructions to apply an exception; and as such, when all limitations in claims 1-14 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-14 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2 and 3 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claim 1 and dependent claims 4-14 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a data processing system (i.e., a computer system) (claim 1); and a hardware storage device (claim 1); receiving data (claim 1); storing data (claim 1); accessing data (claim 1); outputting data (claim 1); transmitting data (claims 4 and 5); and obtaining data (claim 6); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of causing production of a cell population of a second type wherein the second type is different from the first type (claim 6); each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7); the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8); causing production of a stable cell line (claim 9); the stable cell line comprises HEK293 cells (claim 10); a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11); cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12); an automated or semi-automated production (claim 13); and the production is in a closed or semi-closed system (claim 14); are conventional. Evidence of conventionality is shown by:
Tan et al. (“HEK293 cell line as a platform to produce recombinant proteins and viral vectors.” Frontiers in Bioengineering and Biotechnology, 2021, vol. 9, article 796991, pp. 1-9);
Moutsatsou et al. (“Automation in cell and gene therapy manufacturing: from past to future.” Biotechnology Letters, 2019, vol. 41, pp. 1245-1253); and
Zalai et al. (“Advanced development strategies for biopharmaceutical cell culture processes.” Current Pharmaceutical Biotechnology, 2015, vol. 16, pp. 983-1001).
Tan et al. reviews the use of HEK293 cells and its subtypes in the production of biotherapeutics, and compares their usage against other commonly used host cell lines in each category of biotherapeutics and summarizes the factors influencing the choice of host cell lines used (Abstract). Tan et al. shows that since 2015, there have been seven HEK-derived products approved by the FDA, and of these, six are cell and gene therapies where the HEK293 cell line or its derivatives were used in the production of viral vectors (page 1, Introduction; and Table 1). Tan et al. further shows that stable producer lines are preferred for the large-scale production of recombinant proteins, and that the most recent HEK-produced recombinant protein therapeutic was produced in HEK293F cells by transfecting the HEK293F cells with a B-domain deleted human FVIII expression construct, and after stable transfectants were selected, clones exhibiting optimal production were selected for use (page 2, col. 1, bottom, and col. 2, top). Tan et al. further shows various production scales comprising 10 cm cell culture dishes and 2L Erlenmeyer flasks (Tables 2 & 3) and 20L WAVE Bioreactor (Table 4).
Moutsatsou et al. reviews how automation can help address the manufacturing issues arising from the development of large-scale manufacturing processes for modern cell and gene therapy, and summarizes and evaluates the existing automated technologies with applicability in cell and gene therapy (Abstract). Moutsatsou et al. shows that automation can refer to many approaches: automation of one step alone, integration of several steps in one machine (1st generation) or fully automated (2nd generation), and that the term “fully automated” refers to a platform or process which apart from eliminating manual operators for culturing cells, it also eliminates the need for manual transfer of materials from one-unit operation to another (page 1247, col. 2, para. 1). Moutsatsou et al. further shows a closed, automated system (page 1250, col. 1, para. 3); a fully automated production unit for reprogramming, cultivation and differentiation of induced pluripotent stems cells (iPSCs) with a capability to process up to 60 different iPSC lines in parallel (page 1250, col. 1, para. 4); and a fully automated manufacturing and banking of cell therapies that is a fully enclosed platform (page 1250, col. 1, para. 5). Moutsatsou et al. further shows that another important advantage of 2nd generation automated cell manufacturing platforms is the increased flexibility and modularity, particularly hardware modules that are integrated in the platform via agents into the control software using a plug-and-produce approach and software that is adaptable to different applications (i.e., different process conditions or different cells) (page 1250, col. 2, para. 4).
Zalai et al. reviews advanced development strategies for biopharmaceutical cell culture processes, with a focus on tools which enable the integration of physiological knowledge into cell culture process development (Abstract) and shows that mechanistic models are mathematical formulations of the internal operation of systems in terms of their constituent parts and mechanisms (page 991, col. 1, para. 4) and further shows a model that has been enhanced by considering different subpopulations, so that monoclonal antibody production could be described after the exponential phase, and that when combined with real-time measurements of extracellular metabolites, such a model could potentially provide real-time information regarding the apoptotic state of different sub-populations (page 991, col. 2, para. 3). Zalai et al. further reviews technological process control strategies (page 994, col. 1, para. 2) including: batch process mode (page 994, col. 1, para. 3); fed-batch process (page 994, col. 1, para. 4); and continuous/perfusion process (page 994, col. 1, paras. 5-6).
Therefore, when taken alone, all additional elements in claims 1-14 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-14 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. (“Hybrid models based on machine learning and an increasing degree of process knowledge: Application to cell culture processes.” Industrial & Engineering Chemistry Research, 2022, vol. 61, pp. 8658-8672).
Independent claim 1 is directed to a method for generating one or more integrated models comprising one or more machine learning models and one or more mechanistic models; optimizing the one or more integrated models; and outputting one or more predictive models for developing or operating a process for a cell or gene therapy.
Dependent claims 2-5 further define aspects of the method steps of generating the one or more integrated models, e.g., particular attributes and properties of the models; arranging the models; and constraining a prediction.
Narayanan et al. is directed to hybrid models based on machine learning models (i.e., data-driven) and mechanistic (i.e., process knowledge) models for cell culture process modeling.
Regarding independent claim 1, Narayanan et al. shows generating a family of hybrid models for cell culture process modeling with varying fractions of process knowledge explicitly encoded in the model, defined as the degree of hybridization, with the two extremes being fully data driven (0%) and fully mechanistic (100%) models (Abstract); comparing the different models based on different metrics: model accuracy, the experimental effort for model development, extrapolation capability, the capability of generating new process understanding, and ease of utilization in practice, and to demonstrate that this could provide an additional degree of freedom for model selection (Abstract). Narayanan et al. further shows key characteristics of in silico and experimental data (Figure 1(A)) and dynamic profiles of the key process variables constituting the experimental data set (Figure 1(C)); the focus is on the biopharmaceutical cell culture processes (page 8659, col. 1, para. 3) and generating a family of hybrid models which incorporate process knowledge and engineering know-how to different extents, i.e., the degree of hybridization (page 8659, col. 2, bottom); a summary of the different models along a Hybridization Axis (Table 1); the design factors and the data collected during the runs (page 8660, Section 2.1.); generation of seven possible hybrid models that reflect attractive possibilities to utilize available knowledge and commonly measured variables in the context of cell cultures (page 8661, col. 2, Section 2.2.); different statistical methods can be used within the hybrid model framework (page. 8662, col. 1, para. 2); different data-driven models, e.g., artificial neural networks (ANNs) (page 8662, col. 1, Section 2.2.1.); hybrid models with inputs to the neural network (page 8662, col. 1, Section 2.2.2.); nonlinear parameter optimization is performed to obtain the parameters for the mechanistic models (page 8662, col. 2, Section 2.2.3.); and output of predictive models with the best predictive capability (page 8664, col. 1, para. 2).
Regarding dependent claims 2-5, Narayanan et al. shows that the hybrid models are solved using a nonlinear parameter estimation problem based on a quasi-Newton optimization algorithm (page 8662, col. 2., para. 1); key process variables include glucose, lactose, NH4, osmolality, and titer (Figure 1(C)); the sequential arrangement of different mechanisms within the hybrid model (Table 1); and adding physical constraints to the model to improve the model’s performance (page 8665, col. 1, para. 3).
Regarding independent claim 1 and dependent claims 2-5, Narayanan et al. does not show the exact sequence and steps for generating the hybrid (i.e., integrated) models.
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. by broadly incorporating essentially the same steps but in a different sequence, as shown by Narayanan et al. and discussed above. One of ordinary skill in the art would have been motivated to modify the methods of Narayanan et al. because Narayanan et al. shows methods for generating a range of hybrid models, allowing for a choice of hybrid models to be used that is based on the goal of model development, e.g., models with a higher degree of hybridization allow for more process interpretation possibilities. This modification would have had a reasonable expectation of success given that Narayanan et al. discloses methods for generating hybrid models comprising machine learning models and mechanistic models for the specific application to cell culture processes.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Sun et al. (“ALVEN: Algebraic learning via elastic net for static and dynamic nonlinear model identification.” Computers and Chemical Engineering, 2020, vol. 143, article 107103, pp. 1-9, as cited in the Information Disclosure Statement received 10 January 2023) and Tan et al. (“HEK293 cell line as a platform to produce recombinant proteins and viral vectors.” Frontiers in Bioengineering and Biotechnology, 2021, vol. 9, article 796991, pp. 1-9, as cited above).
Dependent claim 9 is directed to one or more algebraic learning via elastic net (ALVEN) output models for use in the production of a stable cell line.
Dependent claim 10 further defines the type of stable cell line as HEK293 cells.
Sun et al. is directed to an algebraic learning via elastic net (ALVEN) for static and dynamic nonlinear model identification algorithm that employs automated feature generation including families of ubiquitous chemical and biological nonlinear transformation.
Tan et al. is directed to the use of HEK293 cells and its subtypes in the production of biotherapeutics.
Regarding dependent claims 9 and 10, Narayanan et al. as applied to claims 1-5 above, does not show using an algebraic learning via elastic net (ALVEN) algorithm for output models; or production of a stable cell line comprising HEK293 cells.
Regarding dependent claim 9, Sun et al. shows an ALVEN algorithm that balances model complexity and prediction accuracy through a two-step feature selection procedure, to produce an interpretable model useful for process applications while avoiding overfitting, and that can be generalizable to nonlinear dynamic systems (i.e., Dynamic ALVEN) (Abstract). Sun et al. further shows comparing the model accuracy of the algorithms to well-established machine learning methods for a chemical reactor (Abstract).
Regarding dependent claim 10, Tan et al. shows that since 2015, there have been seven HEK-derived products approved by the FDA, and of these, six are cell and gene therapies where the HEK293 cell line or its derivatives were used in the production of viral vectors (page 1, Introduction; and Table 1). Tan et al. further shows that stable producer lines are preferred for the large-scale production of recombinant proteins (page 2, col. 1, bottom, and col. 2, top).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. as applied to claims 1-5 above, by incorporating methods for using an algorithm to balance model complexity and prediction accuracy, as shown by Sun et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. as applied to claims 1-5 above, with the methods of Sun et al. because Sun et al. shows that the ALVEN (algebraic learning via elastic net) algorithm aids in producing an interpretable model useful for process application while avoiding overfitting. This modification would have had a reasonable expectation of success given that both Narayanan et al. as applied to claims 1-5 above, and Sun et al. disclose methods for improving the predictive capability of a model that is applicable to cell culture processes.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. as applied to claims 1-5 above, by incorporating methods for using a stable cell line such as HEK293 cells, as shown by Tan et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. as applied to claims 1-5 above, with the methods of Tan et al. because Tan et al. shows that stable producer cell lines are preferred for large-scale production processes. This modification would have had a reasonable expectation of success given that both Narayanan et al. as applied to claims 1-5 above, and Tan et al. disclose methods applicable to cell culture processes.
Claims 6-8, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Moutsatsou et al. (“Automation in cell and gene therapy manufacturing: from past to future.” Biotechnology Letters, 2019, vol. 41, pp. 1245-1253, as cited above).
Dependent claims 6-8 further define characteristics of the cell populations.
Dependent claims 13 and 14 further define aspects of the production equipment.
Moutsatsou et al. is directed to how automation can help address the manufacturing issues arising from the development of large-scale manufacturing processes for modern cell and gene therapy.
Regarding dependent claims 6-8, 13, and 14, Narayanan et al. as applied to claims 1-5 above, does not show cell populations, or cell populations of different types, or heterogeneous cell populations, or clonal cell populations, or intracellular or cell surface heterogeneity; and does not show whether the production process is automated or semi-automated, or a closed or semi-closed system.
Regarding dependent claims 6-8, Moutsatsou et al. shows a fully automated production unit for reprogramming, cultivation and differentiation of induced pluripotent stems cells (iPSCs) with a capability to process up to 60 different iPSC lines in parallel (page 1250, col. 1, para. 4) (i.e., iPSCs can be heterogeneous cultures or clonal cultures); and a fully automated manufacturing and banking of cell therapies that is a fully enclosed platform (page 1250, col. 1, para. 5). Moutsatsou et al. further shows that another important advantage of 2nd generation automated cell manufacturing platforms is the increased flexibility and modularity, particularly hardware modules that are integrated in the platform via agents into the control software using a plug-and-produce approach and software that is adaptable to different applications (i.e., different process conditions or different cells) (page 1250, col. 2, para. 4). Moutsatsou et al. further shows examples of closed, automated production systems (page 1250, col. 1, paras. 2-3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. as applied to claims 1-5 above, by incorporating methods for automation in cell and gene therapy manufacturing, as shown by Moutsatsou et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. as applied to claims 1-5 above, with the methods of Moutsatsou et al. because Moutsatsou et al. shows methods for automation that can provide more control over a bioprocess while leading to a more accurate and faster process optimization. This modification would have had a reasonable expectation of success given that both Narayanan et al. as applied to claims 1-5 above, and Moutsatsou et al. disclose methods applicable to cell culture processes.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Zalai et al. (“Advanced development strategies for biopharmaceutical cell culture processes.” Current Pharmaceutical Biotechnology, 2015, vol. 16, pp. 983-1001, as cited above).
Dependent claims 11 and 12 define aspects of the production process, e.g., the scale of a production run; the type of process modes.
Zalai et al. is directed to advanced development strategies for biopharmaceutical cell culture processes, with a focus on tools which enable the integration of physiological knowledge into cell culture process development.
Regarding dependent claims 11 and 12, Narayanan et al. as applied to claims 1-5 above, does not show the scale of a production run; or the types of process control modes.
Regarding dependent claims 11 and 12, Zalai et al. shows technological process control strategies (page 994, col. 1, para. 2) including: batch process mode (page 994, col. 1, para. 3); fed-batch process (page 994, col. 1, para. 4); and continuous/perfusion process (page 994, col. 1, paras. 5-6). Zalai et al. further shows using real-time measurements in bioprocesses via in-situ Raman spectroscopy across different processing scales, from 3 L up to 2000 L (page 993, col. 1, para. 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. as applied to claims 1-5 above, by incorporating methods for incorporating process tools which enable the integration of physiological knowledge into cell culture process development, as shown by Zalai et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. as applied to claims 1-5 above, with the methods of Zalai et al. because Zalai et al. shows methods for advanced process development approaches that can be applied to maximize process performance and to generate process understanding. This modification would have had a reasonable expectation of success given that both Narayanan et al. as applied to claims 1-5 above, and Zalai et al. disclose methods for cell culture processes.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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